In this guide, we will explore various scenarios for working with Gitlab CI/CD Pipelines. We will provide examples of using the most commonly used options when working with CI/CD. The template library will be expanded as needed.
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You can easily find what CI/CD is and why it's needed through a quick internet search. Full documentation on configuring pipelines in GitLab is also readily available. Here, I'll briefly and as seamlessly as possible describe the system's operation from a bird's-eye view:
A developer submits a commit to the repository, creates a merge request through the website, or initiates a pipeline explicitly or implicitly in some other way.
The pipeline configuration selects all tasks whose conditions allow them to run in the current context.
Tasks are organized according to their respective stages.
Stages are executed sequentially, meaning that all tasks within a stage run in parallel.
If a stage fails (i.e., if at least one task in the stage fails), the pipeline typically stops (almost always).
If all stages are completed successfully, the pipeline is considered to have passed successfully.
In summary, we have:
A pipeline: a set of tasks organized into stages, where you can build, test, package code, deploy a ready build to a cloud service, and more.
A stage: a unit of organizing a pipeline, containing one or more tasks.
A task (job): a unit of work in the pipeline, consisting of a script (mandatory), launch conditions, settings for publishing/caching artifacts, and much more.
Consequently, the task when setting up CI/CD is to create a set of tasks that implement all necessary actions for building, testing, and publishing code and artifacts.
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Templates
In this section, we will provide several ready-made templates that you can use as a foundation for writing your own pipeline.
Minimal Scenario
For small tasks consisting of a couple of jobs:
stages:
- build
TASK_NAME:
stage: build
script:
- ./build_script.sh
stages: Describes the stages of our pipeline. In this example, there is only one stage.
TASK_NAME: The name of our job.
stage: The stage to which our job belongs.
script: A set of scripts to execute.
Standard Build Cycle
Typically, the CI/CD process includes the following steps:
Building the package.
Testing.
Delivery.
Deployment.
You can use the following template as a basis for such a scenario:
stages:
- build
- test
- delivery
- deploy
build-job:
stage: build
script:
- echo "Start build job"
- build-script.sh
test-job:
stage: test
script:
- echo "Start test job"
- test-script.sh
delivery-job:
stage: delivery
script:
- echo "Start delivery job"
- delivery-script.sh
deploy-job:
stage: deploy
script:
- echo "Start deploy job"
- deploy-script.sh
Jobs
In this section, we will explore options that can be applied when defining a job. The general syntax is as follows
<TASK_NAME>:
<OPTION1>: ...
<OPTION2>: ...
We will list commonly used options, and you can find the complete list in the official documentation.
Stage
Documentation
This option specifies to which stage the job belongs. For example:
stages:
- build
- test
TASK_NAME:
...
stage: build
TASK_NAME:
...
stage: test
Stages are defined in the stages directive.
There are two special stages that do not need to be defined in stages:
.pre: Runs before executing the main pipeline jobs.
.post: Executes at the end, after the main pipeline jobs have completed.
For example:
stages:
- build
- test
getVersion:
stage: .pre
script:
- VERSION=$(cat VERSION_FILE)
- echo "VERSION=${VERSION}" > variables.env
artifacts:
reports:
dotenv: variables.env
In this example, before starting all build jobs, we define a variable VERSION by reading it from a file and pass it as an artifact as a system variable.
Image
Documentation
Specifies the name of the Docker image to use if the job runs in a Docker container:
TASK_NAME:
...
image: debian:11
Before_script
Documentation
This option defines a list of commands to run before the script option and after obtaining artifacts:
TASK_NAME:
...
before_script:
- echo "Run before_script"
Script
Documentation
The main section where job tasks are executed is described in the script option. Let's explore it further.
Describing an array of commands: Simply list the commands that need to be executed sequentially in your job:
TASK_NAME:
...
script:
- command1
- command2
Long commands split into multiple lines: You may need to execute commands as part of a script (with comparison operators, for example). In this case, a multiline format is more convenient. You can use different indicators:
Using |:
TASK_NAME:
...
script:
- |
command_line1
command_line2
Using >:
TASK_NAME:
...
script:
- >
command_line1
command_line1_continue
command_line2
command_line2_continue
After_script
Documentation
A set of commands that are run after the script, even if the script fails:
TASK_NAME:
...
script:
...
after_script:
- command1
- command2
Artifacts
Documentation
Artifacts are intermediate builds or files that can be passed from one stage to another.
You can specify which files or directories will be artifacts:
TASK_NAME:
...
artifacts:
paths:
- ${PKG_NAME}.deb
- ${PKG_NAME}.rpm
- *.txt
- configs/
In this example, artifacts will include all files with names ending in .txt, ${PKG_NAME}.deb, ${PKG_NAME}.rpm, and the configs directory. ${PKG_NAME} is a variable (more on variables below).
In other jobs that run afterward, you can use these artifacts by referencing them by name, for example:
TASK_NAME_2:
...
script:
- cat *.txt
- yum -y localinstall ${PKG_NAME}.rpm
- apt -y install ./${PKG_NAME}.deb
You can also pass system variables that you defined in a file:
TASK_NAME:
...
script:
- echo -e "VERSION=1.1.1" > variables.env
...
artifacts:
reports:
dotenv: variables.env
In this example, we pass the system variable VERSION with the value 1.1.1 through the variables.env file.
If necessary, you can exclude specific files by name or pattern:
TASK_NAME:
...
artifacts:
paths:
...
exclude:
- ./.git/**/
In this example, we exclude the .git directory, which typically contains repository metadata. Note that unlike paths, exclude does not recursively include files and directories, so you need to explicitly specify objects.
Extends
Documentation
Allows you to separate a part of the script into a separate block and combine it with a job. To better understand this, let's look at a specific example:
.my_extend:
stage: build
variables:
USERNAME: my_user
script:
- extend script
TASK_NAME:
extends: .my_extend
variables:
VERSION: 123
PASSWORD: my_pwd
script:
- task script
In this case, in our TASK_NAME job, we use extends. As a result, the job will look like this:
TASK_NAME:
stage: build
variables:
VERSION: 123
PASSWORD: my_pwd
USERNAME: my_user
script:
- task script
What happened:
stage: build came from .my_extend.
Variables were merged, so the job includes VERSION, PASSWORD, and USERNAME.
The script is taken from the job (key values are not merged; the job's value takes precedence).
Environment
Documentation
Specifies a system variable that will be created for the job:
TASK_NAME:
...
environment:
RSYNC_PASSWORD: rpass
EDITOR: vi
Release
Documentation
Publishes a release on the Gitlab portal for your project:
TASK_NAME:
...
release:
name: 'Release $CI_COMMIT_TAG'
tag_name: '$CI_COMMIT_TAG'
description: 'Created using the release-cli'
assets:
links:
- name: "myprogram-${VERSION}"
url: "https://gitlab.com/master.dmosk/project/-/jobs/${CI_JOB_ID}/artifacts/raw/myprogram-${VERSION}.tar.gz"
rules:
- if: $CI_COMMIT_TAG
Please note that we use the if rule (explained below).
? Read more: CI/CD Pipelines and Infrastructure for E-Health Platform
Rules and Constraints Directives
To control the behavior of job execution, you can use directives with rules and conditions. You can execute or skip jobs depending on certain conditions. Several useful directives facilitate this, which we will explore in this section.
Rules
Documentation
Rules define conditions under which a job can be executed. Rules regulate different conditions using:
if
changes
exists
allow_failure
variables
The if Operator: Allows you to check a condition, such as whether a variable is equal to a specific value:
TASK_NAME:
...
rules:
- if: '$CI_COMMIT_BRANCH == $CI_DEFAULT_BRANCH'
In this example, the commit must be made to the default branch.
Changes: Checks whether changes have affected specific files. This check is performed using the changes option:In this example, it checks if the script.sql file has changed:
TASK_NAME:
...
rules:
- changes:
- script.sql
Multiple Conditions: You can have multiple conditions for starting a job. Let's explore some examples.
a) If the commit is made to the default branch AND changes affect the script.sql file:
TASK_NAME:
...
rules:
- if: '$CI_COMMIT_BRANCH == $CI_DEFAULT_BRANCH'
changes:
- script.sql
b) If the commit is made to the default branch OR changes affect the script.sql file:
TASK_NAME:
...
rules:
- if: '$CI_COMMIT_BRANCH == $CI_DEFAULT_BRANCH'
- changes:
- script.sql
Checking File Existence: Determined using exists:
TASK_NAME:
...
rules:
- exists:
- script.sql
The job will only execute if the script.sql file exists.
Allowing Job Failure: Defined with allow_failure:
TASK_NAME:
...
rules:
- allow_failure: true
In this example, the pipeline continues even if the TASK_NAME job fails.
Conditional Variable Assignment: You can conditionally assign variables using a combination of if and variables:
TASK_NAME:
...
rules:
- if: '$CI_COMMIT_BRANCH == $CI_DEFAULT_BRANCH'
variables:
DEPLOY_VARIABLE: "production"
- if: '$CI_COMMIT_BRANCH =~ demo'
variables:
DEPLOY_VARIABLE: "demo"
When
Documentation
Determines when a job should be run, such as on manual trigger or at specific intervals. The when directive determines when a job should run. Possible values include:
on_success (default): Runs if all previous jobs have succeeded or have allow_failure: true.
manual: Requires manual triggering (a "Run Pipeline" button appears in the GitLab CI/CD panel).
always: Runs always, regardless of previous results.
on_failure: Runs only if at least one previous job has failed.
delayed: Delays job execution. You can control the delay using the start_in directive.
never: Never runs.
Let's explore some examples:
Manual:
TASK_NAME:
...
when: manual
The job won't start until you manually trigger it in the GitLab CI/CD panel.
2. Always:
TASK_NAME:
...
when: always
The job will always run. Useful, for instance, when generating a report regardless of build results.
On_failure:
TASK_NAME:
...
on_failure: always
The job will run if there is a failure in previous stages. You can use this for sending notifications.
Delayed:
TASK_NAME:
...
when: delayed
start_in: 30 minutes
The job will be delayed by 30 minutes.
Never:
TASK_NAME:
...
when: never
The job will never run.
Using with if:
TASK_NAME:
...
rules:
- if: '$CI_COMMIT_BRANCH == $CI_DEFAULT_BRANCH'
when: manual
In this example, the job will only execute if the commit is made to the default branch and an administrator confirms the run.
Needs
Documentation
Allows you to specify conditions for job execution based on the presence of specific artifacts or completed jobs. With rules of this type, you can control the order in which jobs are executed.
Let's look at some examples.
Artifacts: This directive accepts true (default) and false. To start a job, artifacts must be uploaded in previous stages. Using this configuration:
TASK_NAME:
...
needs:
- artifacts: false
...allows the job to start without artifacts.
Job: You can start a job only after another job has completed:
TASK_NAME:
...
needs:
- job: createBuild
In this example, the task will only start after the job named createBuild has finished.
? Read more: Building a Robust CI/CD Pipeline for Cybersecurity Company
Variables
In this section, we will discuss user-defined variables that you can use in your pipeline scripts as well as some built-in variables that can modify the pipeline's behavior.
User-Defined Variables User-defined variables are set using the variables directive. You can define them globally for all jobs:
variables:
PKG_VER: "1.1.1"
Or for a specific job:
TASK_NAME:
...
variables:
PKG_VER: "1.1.1"
You can then use your custom variable in your script by prefixing it with a dollar sign and enclosing it in curly braces, for example:
script:
- echo ${PKG_VER}
GitLab Variables These variables help you control the build process. Let's list these variables along with descriptions of their properties:
Variable Description Example LOG_LEVEL Sets the log level. Options: debug, info, warn, error, fatal, and panic. Lower priority compared to command-line arguments --debug and --log-level. LOG_LEVEL: warning CI_DEBUG_TRACE Enables or disables debug tracing. Takes values true or false. CI_DEBUG_TRACE: true CONCURRENT Limits the number of jobs that can run simultaneously. CONCURRENT: 5 GIT_STRATEGY Controls how files are fetched from the repository. Options: clone, fetch, and none (don't fetch). GIT_STRATEGY: none
Additional Options In this section, we will cover various options that were not covered in other sections.
Workflow: Allows you to define common rules for the entire pipeline. Let's look at an example from the official GitLab documentation:
workflow:
rules:
- if: $CI_COMMIT_TITLE =~ /-draft$/
when: never
- if: $CI_PIPELINE_SOURCE == "merge_request_event"
- if: $CI_COMMIT_BRANCH == $CI_DEFAULT_BRANCH
In this example, the pipeline:
Won't be triggered if the commit title contains "draft."
Will be triggered if the pipeline source is a merge request event.
Will be triggered if changes are made to the default branch of the repository.
Default Values: Defined in the default directive. Options with these values will be used in all jobs but can be overridden at the job level.
default:
image: centos:7
tags:
- ci-test
In this example, we've defined an image (e.g., a Docker image) and tags (which may be required for some runners).
Import Configuration from Another YAML File: This can be useful for creating a shared part of a script that you want to apply to all pipelines or for breaking down a complex script into manageable parts. It is done using the include option and has different ways to load files. Let's explore them in more detail.
a) Local File Inclusion (local):
include:
- local: .gitlab/ci/build.yml
b) Template Collections (template):
include:
- template: Custom/.gitlab-ci.yml
In this example, we include the contents of the Custom/.gitlab-ci.yml file in our script.
c) External File Available via HTTP (remote):
include:
- remote: 'https://gitlab.dmosk.ru/main/build.yml'
d) Another Project:
include:
- project: 'public-project'
ref: main
file: 'build.yml'
!reference tags: Allows you to describe a script and reuse it for different stages and tasks in your CI/CD. For example:
.apt-cache:
script:
- apt update
after_script:
- apt clean all
install:
stage: install
script:
- !reference [.apt-cache, script]
- apt install nginx
update:
stage: update
script:
- !reference [.apt-cache, script]
- apt upgrade
- !reference [.apt-cache, after_script]
Let's break down what's happening in our example:
We created a task called apt-cache. The dot at the beginning of the name tells the system not to start this task automatically when the pipeline is run. The created task consists of two sections: script and after_script. There can be more sections.
We execute the install stage. In one of the execution lines, we call apt-cache (only the commands from the script section).
In the update stage, we call apt-cache twice—the first executes commands from the script section, and the second executes those from the after_script section.
These are the fundamental concepts and options of GitLab CI/CD pipelines. You can use these directives and templates as a starting point for configuring your CI/CD pipelines efficiently. For more advanced use cases and additional options, refer to the official GitLab CI/CD documentation.
Cloud adoption is a crucial consideration for many enterprises. With the need to migrate from on-premises infrastructure to the cloud, businesses seek effective frameworks to streamline this transition. One such framework gaining traction is the Terraform Framework.
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This article delves into the details of the Terraform Framework and its significance, particularly for enterprise-level cloud adoption projects. We will explore the background behind its adoption, the Cloud Adoption Framework for Microsoft, the concept of landing zones, and the four levels of the Terraform Framework.
https://youtu.be/vzCO-h4a9h4
Background and Adoption Strategy
Many large enterprises face the challenge of migrating their infrastructure from on-premises environments to the cloud. In response to this, Microsoft developed the Cloud Adoption Framework (CAF) as a strategic guide for customers to plan, adopt, and implement cloud services effectively.
Let's dive deeper into the components and benefits of the Terraform Framework within the Cloud Adoption Framework.
Understanding the Azure Cloud Adoption Framework (CAF)
The Cloud Adoption Framework for Microsoft (CAF) is a comprehensive framework that assists customers in defining their cloud strategy, planning the adoption process, and continuously implementing and managing cloud services. It covers various aspects of cloud adoption, from migration strategies to application and service management in the cloud. To gain a better understanding of this framework, it is essential to explore its core components.
Landing Zones
A fundamental component of the CAF is the concept of landing zones. A landing zone represents a scaled and secure Azure environment, typically designed for multiple subscriptions. It acts as the building block for the overall infrastructure landscape, ensuring proper connectivity and security between different application components and even on-premises systems. Landing zones consist of several elements, including security measures, governance policies, management and monitoring services, and application-specific services within a subscription.
CAF and Infrastructure Organization
The Microsoft documentation on CAF outlines different approaches to cloud adoption based on the size and complexity of an organization. Small organizations utilizing a single subscription in Azure will have a different adoption approach compared to large enterprises with numerous services and subscriptions. For enterprise-level deployments, an organized infrastructure landscape is crucial. This includes creating management groups and subscription organization, each serving specific governance and security requirements. Additionally, specialized subscriptions, such as identity subscriptions, management subscriptions, and connectivity subscriptions, are part of the overall landing zone architecture.
? Discover the power of Caf-Terraform, a revolutionary framework that takes your infrastructure management to the next level. Let's dive in!
The Four Levels of the Terraform Framework
The Terraform Framework, an open-source project developed by Microsoft architects and engineers, simplifies the deployment of landing zones within Azure. It consists of four main components: rover, models, landing zones, and launchpad.
a. Rover:
The rover is a Docker container that encapsulates all the necessary tools for infrastructure deployment. It includes Terraform itself and additional scripts, facilitating a seamless transition to CI/CD pipelines across different platforms. By utilizing the rover, teams can standardize deployments and avoid compatibility issues caused by different Terraform versions on individual machines.
b. Models:
The models represent cloud adoption framework templates, hosted within the Terraform registry or GitHub repositories. These templates cover a wide range of Azure resources, providing a standardized approach for deploying infrastructure components. Although they may not cover every single resource available in Azure, they offer a strong foundation for most common resources and are continuously updated and supported by the community.
c. Landing Zones:
Landing zones represent compositions of multiple resources, services, or blueprints within the context of the Terraform Framework. They enable the creation of complex environments by dividing them into manageable subparts or services. By modularizing landing zones, organizations can efficiently deploy and manage infrastructure based on their specific requirements. The Terraform state file generated from the landing zone provides valuable information for subsequent deployments and configurations.
d. Launchpad:
The launchpad serves as the starting point for the Terraform Framework. It comprises scripts and Terraform configurations responsible for creating the foundational components required for all other levels. By deploying the launchpad, organizations establish storage accounts, keywords, and permissions necessary for storing and managing Terraform state files for higher-level deployments.
Understanding the Communication between Levels
To ensure efficient management and organization, the Terraform Framework promotes a layered approach, divided into four levels:
Level Zero: This level represents the launchpad and focuses on establishing the foundational infrastructure required for subsequent levels. It involves creating storage accounts, setting up subscriptions, and permissions for managing state files.
Level One: Level one primarily deals with security and compliance aspects. It encompasses policies, access control, and governance implementation across subscriptions. The level one pipeline reads outputs from level zero but has read-only access to the state files.
Level Two: Level two revolves around network infrastructure and shared services. It includes creating hub networks, configuring DNS, implementing firewalls, and enabling shared services such as monitoring and backup solutions. Level two interacts with level one and level zero, retrieving information from their state files.
Level Three and Beyond: From level three onwards, the focus shifts to application-specific deployments. Development teams responsible for application infrastructure, such as Kubernetes clusters, virtual machines, or databases, engage with levels three and beyond. These levels have access to state files from the previous levels, enabling seamless integration and deployment of application-specific resources.
Simplifying Infrastructure Deployments
In order to create new virtual machines for specific applications, we can leverage the power of Terraform and modify the configuration inside the Terraform code. By doing so, we can trigger a pipeline that resembles regular Terraform work. This approach allows us to have more control over the deployment and configuration of virtual machines.
Streamlining Service Composition and Environment Delivery
When discussing service composition and delivering a complete environment, this layered approach in Terraform can be quite beneficial. We can utilize landing zones or blueprint models at different levels. These models have input variables and produce output variables that are saved into the Terraform state file. Another landing zone or level can access these output variables, use them within its own logic, compose them with input variables, and produce its own output variables.
Organizing Teams and Repositories
This layered approach, facilitated by Terraform, helps to organize the relationship between different repositories or teams within an organization. Developers or DevOps professionals responsible for creating landing zones or cleaning zones can work locally with the Rover container in VS Code. They write Terraform code, compose and utilize modules, and create landing zone logic.
Separation of Logic and Configuration
The logic and configuration in the Terraform code are split into separate files, similar to regular Terraform practices. The logic is stored in .tf and .tfvars files, while the configuration is stored in .tfvars files, which can be organized into different environments. This separation allows for better management and maintainability.
Empowering Application Teams
Within an organization, different teams can be responsible for different aspects of the infrastructure. An experienced Azure team can define the organization's standards and write the landing zone logic using Terraform. They can provide examples of configuration files that application teams can use. By offloading the configuration files to the application teams, they can easily create infrastructure for their applications without directly involving the operations team.
Standardization and Unification
This approach allows for the standardization and unification of infrastructure within the organization. With the use of modules in Terraform, teams don't have to start from scratch but can reuse existing code and configurations, creating a consistent and streamlined infrastructure landscape.
Challenges and Considerations
Working with Terraform and the Caf-terraform framework may have some complexities. For example, the Rover tool is not able to work with managed identities, requiring the management of service principals in addition to containers and managed identities. Additionally, there may be some bugs in the modules that need to be addressed, but the open-source nature of the framework allows for contributions and improvements. Understanding the framework and its intricacies may take some time due to the documentation being spread across multiple reports and components.
Key components and features of CAF Terraform:
ComponentDescriptionCloud Adoption Framework (CAF)Microsoft's framework that provides guidance and best practices for organizations adopting Azure cloud services.TerraformOpen-source infrastructure-as-code tool used for provisioning and managing cloud resources.Azure Landing ZonesPre-configured environments in Azure that provide a foundation for deploying workloads securely and consistently.Infrastructure as Code (IaC)Approach to defining and managing infrastructure resources using declarative code.Standardized DeploymentsEnsures consistent configurations and deployments across environments, reducing inconsistencies and human errors.ModularityOffers a modular architecture allowing customization and extension of the framework based on organizational requirements.CustomizabilityEnables organizations to adapt and tailor CAF Terraform to their specific needs, incorporating existing processes, policies, and compliance standards.Security and GovernanceEmbeds security controls, network configurations, identity management, and compliance requirements into infrastructure code to enforce best practices and ensure secure deployments.Ongoing ManagementSimplifies ongoing management, updates, and scaling of Azure landing zones, enabling organizations to easily make changes to configurations and manage the lifecycle of resources.Collaboration and AgilityFacilitates collaboration among teams through infrastructure-as-code practices, promoting agility, version control, and rapid deployments.Documentation and CommunityComprehensive documentation and resources provided by Microsoft Azure, along with a vibrant community offering tutorials, examples, and support for leveraging CAF Terraform effectively.This table provides an overview of the key components and features of CAF Terraform
Conclusion: Azure Cloud Adoption Framework
The Terraform Framework within the Cloud Adoption Framework (CAF) offers enterprises a powerful toolset for cloud adoption and migration projects. By leveraging the modular structure of landing zones and adhering to the layered approach, organizations can effectively manage infrastructure deployments in Azure. The Terraform Framework's components, including rover, models, landing zones, and launchpad, contribute to standardization, automation, and collaboration, leading to successful cloud adoption and improved operational efficiency.
As organizations embrace the cloud, the Caf-terraform framework provides a layered approach to managing infrastructure and deployments. By separating logic and configuration and leveraging modules, it allows for standardized and unified infrastructure across teams and repositories. This framework simplifies and optimizes the transition from on-premises to the cloud, enabling enterprises to harness the full potential of Azure's capabilities.
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In this blog post, we will delve into the intricacies of on-premise to cloud migration, demystifying the process and providing you with a comprehensive guide. Whether you're a business owner, an IT professional, or simply curious about cloud migration, this post will equip you with the knowledge and tools to navigate the migration journey successfully.
How Cloud Migration Affects Your Business?
The impact of cloud migration on your company refers to the process of shifting operations from on-premise installations to the cloud. This migration involves transferring data, programs, and IT processes from an on-premise data center to a cloud-based infrastructure.
Similar to a physical relocation, cloud migration offers benefits such as cost savings and enhanced flexibility, surpassing those typically experienced when moving from a smaller to a larger office. The advantages of cloud migration can have a significant positive impact on businesses.
Pros and cons of on-premise to cloud migration
ProsConsScalabilityConnectivity dependencyCost savingsMigration complexityAgility and flexibilityVendor lock-inEnhanced securityPotential learning curveImproved collaborationDependency on cloud provider's reliabilityDisaster recovery and backupCompliance and regulatory concernsHigh availability and redundancyData transfer and latencyInnovation and latest technologiesOngoing operational costsTable summarizing the key aspects of on-premise to cloud migration
Looking for On-Premise to Cloud Migration? Contact Gart Today!
Gart's Successful On-Premise to Cloud Migration Projects
Optimizing Costs and Operations for Cloud-Based SaaS E-Commerce Platform
In this case study, you can find the journey of a cloud-based SaaS e-commerce platform that sought to optimize costs and operations through an on-premise to cloud migration. With a focus on improving efficiency, user experience, and time-to-market acceleration, the client collaborated with Gart to migrate their legacy platform to the cloud.
By leveraging the expertise of Gart's team, the client achieved cost optimization, enhanced flexibility, and expanded product offerings through third-party integrations. The case study highlights the successful transformation, showcasing the benefits of on-premise to cloud migration in the context of a SaaS e-commerce platform.
Read more: Optimizing Costs and Operations for Cloud-Based SaaS E-Commerce Platform
Implementation of Nomad Cluster for Massively Parallel Computing
This case study highlights the journey of a software development company, specializing in Earth model construction using a waveform inversion algorithm. The company, known as S-Cube, faced the challenge of optimizing their infrastructure and improving scalability for their product, which analyzes large amounts of data in the energy industry.
This case study showcases the transformative power of on-premise to AWS cloud migration and the benefits of adopting modern cloud development techniques for improved infrastructure management and scalability in the software development industry.
Through rigorous testing and validation, the team demonstrated the system's ability to handle large workloads and scale up to thousands of instances. The collaboration between S-Cube and Gart resulted in a new infrastructure setup that brings infrastructure management to the next level, meeting the client's goals and validating the proof of concept.
Read more: Implementation of Nomad Cluster for Massively Parallel Computing
Understanding On-Premise Infrastructure
On-premise infrastructure refers to the physical hardware, software, and networking components that are owned, operated, and maintained within an organization's premises or data centers. It involves deploying and managing servers, storage systems, networking devices, and other IT resources directly on-site.
Pros:
Control: Organizations have complete control over their infrastructure, allowing for customization, security configurations, and compliance adherence.
Data security: By keeping data within their premises, organizations can implement security measures aligned with their specific requirements and have greater visibility and control over data protection.
Compliance adherence: On-premise infrastructure offers a level of control that facilitates compliance with regulatory standards and industry-specific requirements.
Predictable costs: With on-premise infrastructure, organizations have more control over their budgeting and can accurately forecast ongoing costs.
Cons:
Upfront costs: Setting up an on-premise infrastructure requires significant upfront investment in hardware, software licenses, and infrastructure setup.
Scalability limitations: Scaling on-premise infrastructure requires additional investments in hardware and infrastructure, making it challenging to quickly adapt to changing business needs and demands.
Maintenance and updates: Organizations are responsible for maintaining and updating their infrastructure, which requires dedicated IT staff, time, and resources.
Limited flexibility: On-premise infrastructure can be less flexible compared to cloud solutions, as it may be challenging to quickly deploy new services or adapt to fluctuating resource demands.
Exploring the Cloud
Cloud computing refers to the delivery of computing resources, such as servers, storage, databases, software, and applications, over the internet. Instead of owning and managing physical infrastructure, organizations can access and utilize these resources on-demand from cloud service providers.
Benefits of cloud computing include:
Cloud services allow organizations to easily scale their resources up or down based on demand, providing flexibility and cost-efficiency.
With cloud computing, organizations can avoid upfront infrastructure costs and pay only for the resources they use, reducing capital expenditures.
Cloud services enable users to access their applications and data from anywhere with an internet connection, promoting remote work and collaboration.
Cloud providers typically offer robust infrastructure with high availability and redundancy, ensuring minimal downtime and improved reliability.
Cloud providers implement advanced security measures, such as encryption, access controls, and regular data backups, to protect customer data.
Cloud Deployment Models: Public, Private, Hybrid
When considering a cloud migration strategy, it's essential to understand the various deployment models available. Cloud deployment models determine how cloud resources are deployed and who has access to them. Understanding these deployment models will help organizations make informed decisions when determining the most suitable approach for their specific needs and requirements.
Deployment ModelDescriptionBenefitsConsiderationsPublic CloudCloud services provided by third-party vendors over the internet, shared among multiple organizations.- Cost efficiency - Scalability - Reduced maintenance- Limited control over infrastructure - Data security concerns - Compliance considerationsPrivate CloudCloud infrastructure dedicated to a single organization, either hosted on-premise or by a third-party provider.- Enhanced control and customization - Increased security - Compliance adherence- Higher upfront costs - Requires dedicated IT resources for maintenance - Limited scalability compared to public cloudHybrid CloudCombination of public and private cloud environments, allowing organizations to leverage benefits from both models.- Flexibility to distribute workloads - Scalability options - Customization and control- Complexity in managing both environments - Potential integration challenges- Data and application placement decisionsTable summarizing the key characteristics of the three cloud deployment models
Cloud Service Models (IaaS, PaaS, SaaS)
Cloud computing offers a range of service models, each designed to meet different needs and requirements. These service models, known as Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS), provide varying levels of control and flexibility for organizations adopting cloud technology.
Infrastructure as a Service (IaaS)
IaaS provides virtualized computing resources, such as virtual machines, storage, and networking infrastructure. Organizations have control over the operating systems, applications, and middleware while the cloud provider manages the underlying infrastructure.
Platform as a Service (PaaS)
PaaS offers a platform and development environment for building, testing, and deploying applications. It abstracts the underlying infrastructure, allowing developers to focus on coding and application logic rather than managing servers and infrastructure.
Software as a Service (SaaS)
SaaS delivers fully functional applications over the internet, eliminating the need for organizations to install, maintain, and update software locally. Users can access and use applications through a web browser.
Key Cloud Providers and Their Offerings
Selecting the right cloud provider is a critical step in ensuring a successful migration to the cloud. With numerous options available, organizations must carefully assess their requirements and evaluate cloud providers based on key factors such as offerings, performance, pricing, vendor lock-in risks, and scalability options.
Amazon Web Services (AWS): Offers a wide range of cloud services, including compute, storage, database, AI, and analytics, through its AWS platform.
Microsoft Azure: Provides a comprehensive set of cloud services, including virtual machines, databases, AI tools, and developer services, on its Azure platform.
Google Cloud Platform (GCP): Offers cloud services for computing, storage, machine learning, and data analytics, along with a suite of developer tools and APIs.
Read more: How to Choose Cloud Provider: AWS vs Azure vs Google Cloud
Checklist for Preparing for Cloud Migration
Assess your current infrastructure, applications, and data to understand their dependencies and compatibility with the cloud environment.
Identify specific business requirements, scalability needs, and security considerations to align them with the cloud migration goals.
Anticipate potential migration challenges and risks, such as data transfer limitations, application compatibility issues, and training needs for IT staff.
Develop a well-defined migration strategy and timeline, outlining the step-by-step process of transitioning from on-premise to the cloud.
Consider factors like the sequence of migrating applications, data, and services, and determine any necessary dependencies.
Establish a realistic budget that covers costs associated with data transfer, infrastructure setup, training, and ongoing cloud services.
Allocate resources effectively, including IT staff, external consultants, and cloud service providers, to ensure a seamless migration.
Evaluate and select the most suitable cloud provider based on your specific needs, considering factors like offerings, performance, and compatibility.
Compare pricing models, service level agreements (SLAs), and security measures of different cloud providers to make an informed decision.
Examine vendor lock-in risks and consider strategies to mitigate them, such as using standards-based approaches and compatibility with multi-cloud or hybrid cloud architectures.
Consider scalability options provided by cloud providers to accommodate current and future growth requirements.
Ensure proper backup and disaster recovery plans are in place to protect data during the migration process.
Communicate and involve stakeholders, including employees, customers, and partners, to ensure a smooth transition and minimize disruptions.
Test and validate the migration plan before executing it to identify any potential issues or gaps.
Develop a comprehensive training plan to ensure the IT staff is equipped with the necessary skills to manage and operate the cloud environment effectively.
Ready to unlock the benefits of On-Premise to Cloud Migration? Contact Gart today for expert guidance and seamless transition to the cloud. Maximize scalability, optimize costs, and elevate your business operations.
Cloud Migration Strategies
When planning a cloud migration, organizations have several strategies to choose from based on their specific needs and requirements. Each strategy offers unique benefits and considerations.
Lift-and-Shift Migration
The lift-and-shift strategy involves migrating applications and workloads from on-premise infrastructure to the cloud without significant modifications. This approach focuses on rapid migration, minimizing changes to the application architecture. It offers a quick transition to the cloud but may not fully leverage cloud-native capabilities.
Replatforming
Replatforming, also known as lift-and-improve, involves migrating applications to the cloud while making minimal modifications to optimize them for the target cloud environment. This strategy aims to take advantage of cloud-native services and capabilities to improve scalability, performance, and efficiency. It strikes a balance between speed and optimization.
Refactoring (Cloud-Native)
Refactoring, or rearchitecting, entails redesigning applications to fully leverage cloud-native capabilities and services. This approach involves modifying the application's architecture and code to be more scalable, resilient, and cost-effective in the cloud. Refactoring provides the highest level of optimization but requires significant time and resources.
Hybrid Cloud
A hybrid cloud strategy combines on-premise infrastructure with public and/or private cloud resources. Organizations retain some applications and data on-premise while migrating others to the cloud. This approach offers flexibility, allowing businesses to leverage cloud benefits while maintaining certain sensitive or critical workloads on-premise.
Multi-Cloud
The multi-cloud strategy involves distributing workloads across multiple cloud providers. Organizations utilize different cloud platforms simultaneously, selecting the most suitable provider for each workload based on specific requirements. This strategy offers flexibility, avoids vendor lock-in, and optimizes services from various cloud providers.
Cloud Bursting
Cloud bursting enables organizations to dynamically scale their applications from on-premise infrastructure to the cloud during peak demand periods. It allows seamless scalability by leveraging additional resources from the cloud, ensuring optimal performance and cost-efficiency.
Data Replication and Disaster Recovery
This strategy involves replicating and synchronizing data between on-premise systems and the cloud. It ensures data redundancy and enables efficient disaster recovery capabilities in the cloud environment.
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Read more: Cloud vs. On-Premises: Choosing the Right Path for Your Data
